Industry is increasingly relying on analytics and prediction systems to predict events and business outcomes. With these predictions, businesses hope to preempt problems and improve business performance. However, such analytics systems are becoming increasingly complex, limiting usability by managers.
In particular, subprocesses within the analytics system, such as data preprocessing and modeling, utilize complex algorithms and techniques, each using a variety of parameters and factors that influence functionality. For example, industry is increasingly turning to unstructured data sources that are preprocessed using a variety of interpreters and algorithms, each utilizing a different set of parameters and providing a different output.
As such, the complexity of conventional analytics systems often prevents use of such systems by business management. Further, the expense associated with using expert labor to perform analysis and yield predictions leads to less frequent use and lowers the cost effectiveness of such systems.